Math-Physical Medicine

NO. 111

A quantitative clinical case study of geriatric concerns for the gastrointestinal system metabolism and body weight using the GH-Method: math-physical medicine

Corresponding Author: Gerald C. Hsu, eclaireMD Foundation, USA.

This paper addresses certain geriatric concerns related to gastrointestinal system metabolism and body weight using the GH-Method: math-physical medicine (MPM) approach.

A person’s weight is the final result of his or her metabolic system performance which includes inputs of food portions and water intake with outputs of exercises and bowel movements.  Other influential factors such as urination, sweat, sleep, stress, and illness play a role in it.

For seniors, their metabolism slows down which causes many bio-mark changes accordingly, including body weight.  This paper discusses a 72-year old male, type 2 diabetes patient’s overall health state, especially the relationships among his weight, food portion, exercise, and bowel movement.

For the period of 1/2/2012 through 8/22/2019, the author recorded his daily morning weight and walking steps (his main exercise).  Starting on 4/11/2015, he has collected his food portion data, including three meals and fruit or snack.  Beginning on 7/1/2016, he started to collect his daily bowel movement data.  For considerations of data integrity and data relevancy, he selected the period of 7/1/2016 through 8/22/2019 (ages 69 through 72) for his geriatric analysis.  However, in order to study the relationship between weight and exercise, he selected a longer period of 12/1/2013 through 8/22/2019 in order to see the long-term effect of exercise on weight.

Figures 1 and 2 show that for the period of 2012-2015, weight was reduced due to both decreased food portion and increased exercise.  During the period of 2016-2019, although exercise has been maintained at 17,000 steps (7 miles) per day and food portion has been reduced from 90% to 78% of his “normal” portion, i.e. around age of 63, his weight stayed approximately 173 lbs. with a BMI of 25.5.  This phenomenon is due to his slower metabolism in the gastrointestinal system.  The reason he continues to reduce his food portion is that he wanted to bring down his weight to 166 lbs. with a BMI of 24.5.

Figure 1: Weight change due to food portion (before 2015 and after 2016)
Figure 2: Weight change due to exercise (before 2015 and after 2016)

Figure 3 shows the complicated nonlinear and dynamic relationships among weight, food portion, exercise, and bowel movement.  Therefore, readers cannot easily decipher this chart due to their inherited complex relationships. 

The author has developed an AI-based (artificial intelligence) weight prediction model using three consecutive days of data based on weight, food portion, and bowel movement with machine learning and auto-correction capabilities.  From Figure 4, it is obvious that his prediction model is highly accurate.  The correlation coefficient and linear accuracy between predicted weight and measured weight are 86.2% and 99.7% (172.81 lbs. vs. 172.31 lbs.), respectively.  This weight prediction model offers him an effective tool to control his weight and furthermore maintain his glucose level. 

Figure 3: Complex relationships among weight, food, exercise, and bowel movement
Figure 4: Predicted weight vs. measured weight (5/11/2015 - 8/22/2019)

This quantitative clinical case study shows the impact of slower metabolism on the gastrointestinal system in regard to bodyweight.  This geriatric phenomenon has been observed from his age 69 to 72.  For a younger adult, the combination of food portion control, rigorous exercise, and daily smooth bowel movement can almost guarantee his/her weight reduction, but for seniors, this is not the case.

This article has demonstrated two points.  First, geriatrics involves many branches of medicine, but its key character is the metabolic change due to aging.  Second, the author’s GH-Method: math-physical medicine can be utilized for geriatrics research as well.